Can ice-cliffs explain the debris-cover anomaly? New insightsfrom Changri Nup Glacier, Nepal, Central Himalaya
Bibliographic record
Abstract
Abstract. Ice cliff backwasting on debris-covered glaciers is recognized as an important process, potentially responsible for the so-called debris-cover anomaly, i.e. the fact that debris-covered and debris-free glacier tongues appear to have similar thinning rates in Himalaya. In this study, we assess the total contribution of ice cliff backwasting to the net ablation of the tongue of the Changri Nup Glacier over two years. Detailed terrestrial photogrammetry surveys were conducted on select ice cliffs in November 2015 and 2016, and the entire glacier tongue was surveyed with unmanned air vehicles (UAVs) and Pléiades tri-stereo imagery in November 2015, November 2016, and November 2017. The total difference between the volume loss from ice cliffs measured with the terrestrial photogrammetry, considered as the reference data, and the UAV and Pléiades was less than 3 % and 7 %, respectively, demonstrating the ability of these datasets to measure volume loss from ice cliffs. For the period November 2015–November 2016 (resp. November 2016–November 2017), using UAV and Pléiades over the entire glacier tongue, we found that ice cliffs, which cover 7 % (resp. 8 %) of the planar area, contribute to 23 ± 5 % (resp. 24 ± 5 %) of the total net ablation of Changri Nup Glacier. Ice cliffs have a net ablation rate 3.1 ± 0.6 (resp. 3.0 ± 0.6) times higher than the average glacier tongue surface. However, on Changri Nup Glacier, ice cliffs cannot compensate for the reduction of ablation due to debris-cover. Reduced ablation and lower emergence velocities on debris-covered glacier tongues could be responsible for the debris-cover anomaly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".